70/100

Moderate evidence

ProblemPublished recordScienceProblem Space

A Meta-Analysis of the Acceptance of Artificial Intelligence in Psychotherapy

Truvace record
TRV-2026-2732raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
sha256
06502c5fe292…check it

In recent years, artificial intelligence (AI) technology has demonstrated potential in psychotherapy. However, there is currently a lack of systematic research that comprehensively assessed public acceptance of AI-based psychotherapy. The purpose of this study is to use meta-analysis to explore public acceptance of AI in psychotherapy and the sources of heterogeneity. A random-effects model was employed, using Hedges' g as the effect size, to pool the differences in acceptance between the AI group and the conven…

The problem

A meta-analysis of 10 studies with 1,967 participants found no statistically significant difference in acceptance between AI psychotherapy and conventional therapy (g = −0.171, 95% CI −0.908 to 0.566), and later and larger studies reported lower acceptance of AI.

What this doesn’t fix

The meta-analysis included only 10 studies with very high heterogeneity (I² = 98%) and very low certainty of evidence. Possible publication bias and an unregistered protocol add further uncertainty, and the moderator findings are exploratory.

Sources

1 on file
  1. Behavioral Sciencesdoi:10.3390/bs16091552
    Peer-reviewed2026-09-02

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

  • Public acceptance of AI-based psychotherapy compared with conventional psychotherapy

    Hedges' g −0.171 (95% CI −0.908 to 0.566)

    10 studies · 1,967 people

    The review’s own words
    The meta-analysis results showed that the overall effect size was g = −0.171 (95% CI: [−0.908, 0.566], p = 0.649)

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results

Cite this record

v1

Truvace Impact Record TRV-2026-2732, v1: “A Meta-Analysis of the Acceptance of Artificial Intelligence in Psychotherapy.” Truvace, 2026-11-12. /record/TRV-2026-2732 (accessed on the day you copy it). sha256 06502c5fe292d1b0…

Change history

1 version

Every change made to this record since it was first published, and why. Nothing has changed since it went up.

  1. Publishedv106502c5fe292…

    Certified into the record

Check this record hasn’t been altered

Check this record hasn’t been altered
How to check it yourself, without trusting this page

Download any version from /api/record/TRV-2026-2732 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.